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R Programming - (LAB PROGRAMS)


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Aim:

☛  

Solution :

Week 9

CSV file : "student_scores.csv" 

Hours,Preparation,IQ,Score
2,3,110,50
4,4,105,60
6,5,115,65
8,6,120,80
10,8,125,90
12,9,130,95
14,10,100,90

In the above CSV file,
Hours: Study hours
Preparation: Days of preparation
IQ: Intelligence score
Score: Final exam score (target variable)

  To Download above CSV file : Click Here

Simple Linear Regression

R Program: Linear_Regression.R

# Load required library
if(!require(ggplot2)) install.packages("ggplot2")
library(ggplot2)

# Read the CSV file
data <- read.csv("student_scores.csv")

# View the data
cat("Dataset:\n")
print(data)

# Simple Linear Regression (Score ~ Hours)
model_linear <- lm(Score ~ Hours, data = data)
cat("\nSimple Linear Regression Summary:\n")
print(summary(model_simple))

# Plotting the regression line
plot(data$Hours, data$Score, main = "Simple Linear Regression",
     xlab = "Study Hours", ylab = "Score", pch = 16, col = "blue")
abline(model_linear, col = "red", lwd = 2)

Output:

Dataset:
  Hours Preparation  IQ Score
1     2           3 110    50
2     4           4 105    60
3     6           5 115    65
4     8           6 120    80
5    10           8 125    90
6    12           9 130    95
7    14          10 100    90

Simple Linear Regression Summary:

Call:
lm(formula = Score ~ Hours, data = data)

Residuals:
      1       2       3       4       5       6 
 0.2381  0.8095 -3.6190  1.9524  2.5238 -1.9048 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  40.3333     2.4462   16.49 7.92e-05 ***
Hours         4.7143     0.3141   15.01 0.000115 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.628 on 4 degrees of freedom
Multiple R-squared:  0.9826,    Adjusted R-squared:  0.9782 
F-statistic: 225.3 on 1 and 4 DF,  p-value: 0.0001148

Linear Regression

 

Multiple Linear Regression

R Program: M_Linear_Regression.R

# Load required libraries
if(!require(scatterplot3d)) install.packages("scatterplot3d")
library(scatterplot3d)

# Read the dataset
data <- read.csv("student_scores.csv")

# Multiple Linear Regression model
model_multi <- lm(Score ~ Hours + Preparation + IQ, data = data)
cat("Multiple Linear Regression Summary:\n")
print(summary(model_multi))

# Predict the fitted values
predicted_scores <- predict(model_multi)

# 3D Scatter Plot: using Hours and Preparation as predictors
s3d <- scatterplot3d(data$Hours, data$Preparation, data$Score,
                     pch = 19, color = "blue",
                     xlab = "Hours", ylab = "Preparation", zlab = "Score",
                     main = "3D Plot: Hours & Preparation vs Score",
                     highlight.3d = TRUE, angle = 50)

# Add predicted values as a regression line
s3d$points3d(data$Hours, data$Preparation, predicted_scores,
             col = "red", type = "l", lwd = 2)

Output:

Multiple Linear Regression Summary:

Call:
lm(formula = Score ~ Hours + Preparation + IQ, data = data)

Residuals:
      1       2       3       4       5       6       7 
-2.0056  3.0877 -3.3955  2.3134  2.5093 -1.7817 -0.7276 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  -4.3470    16.9883  -0.256   0.8146  
Hours         3.2929     3.4852   0.945   0.4145  
Preparation   0.5131     5.7375   0.089   0.9344  
IQ            0.4384     0.1453   3.017   0.0569 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.671 on 3 degrees of freedom
Multiple R-squared:  0.9778,    Adjusted R-squared:  0.9556 
F-statistic: 44.04 on 3 and 3 DF,  p-value: 0.005578

Multiple Linear Regression

 

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